{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes:\n",
    " * tuned fresh and trained on toi data, with toi examples in the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/1/AstroCNNModelVetting_vrevised_tuned_20211023_194357',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/2/AstroCNNModelVetting_vrevised_tuned_20211023_200854',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/3/AstroCNNModelVetting_vrevised_tuned_20211023_203347',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/4/AstroCNNModelVetting_vrevised_tuned_20211023_205843',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/5/AstroCNNModelVetting_vrevised_tuned_20211023_212345',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/6/AstroCNNModelVetting_vrevised_tuned_20211023_214902',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/7/AstroCNNModelVetting_vrevised_tuned_20211023_221411',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/8/AstroCNNModelVetting_vrevised_tuned_20211023_223911',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/9/AstroCNNModelVetting_vrevised_tuned_20211023_230410',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/10/AstroCNNModelVetting_vrevised_tuned_20211023_232907']"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "chkpt_root = '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2'\n",
    "data_files = '/mnt/tess/astronet/tfrecords-vetting-6-toi-val/*'\n",
    "tces_file = '/mnt/tess/astronet/tces-vetting-v5-toi-val.csv'\n",
    "\n",
    "nruns = 10\n",
    "\n",
    "def load_ensemble(chkpt_root, nruns):\n",
    "    checkpts = []\n",
    "    for i in range(nruns):\n",
    "        parent = os.path.join(chkpt_root, str(i + 1))\n",
    "        if not os.path.exists(parent):\n",
    "            break\n",
    "        all_dirs = os.listdir(parent)\n",
    "        if not all_dirs:\n",
    "            break\n",
    "        d, = all_dirs\n",
    "        checkpts.append(os.path.join(parent, d))\n",
    "    return checkpts\n",
    "\n",
    "paths = load_ensemble(chkpt_root, nruns)\n",
    "paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running model 1\n",
      "354 records\n",
      "Running model 2\n",
      "354 records\n",
      "Running model 3\n",
      "354 records\n",
      "Running model 4\n",
      "354 records\n",
      "Running model 5\n",
      "354 records\n",
      "Running model 6\n",
      "354 records\n",
      "Running model 7\n",
      "354 records\n",
      "Running model 8\n",
      "354 records\n",
      "Running model 9\n",
      "354 records\n",
      "Running model 10\n",
      "354 records\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "import os\n",
    "from astronet import predict\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "def run_predictions(path):\n",
    "    predict.FLAGS = predict.parser.parse_args([\n",
    "      '--model_dir', path,\n",
    "      '--data_files', data_files,\n",
    "      '--output_file', '',\n",
    "    ])\n",
    "\n",
    "    return predict.predict()\n",
    "\n",
    "\n",
    "paths = load_ensemble(chkpt_root, nruns)\n",
    "ensemble_preds = []\n",
    "config = None\n",
    "for i, path in enumerate(paths):\n",
    "    print(f'Running model {i + 1}')\n",
    "    preds, config = run_predictions(path)\n",
    "    ensemble_preds.append(preds.set_index('tic_id'))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['disp_p', 'disp_e', 'disp_n']\n",
    "\n",
    "col_p = labels.index('disp_p')\n",
    "# thresh = 0.1579816417694639\n",
    "thresh = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds_table = ensemble_preds[0].copy()\n",
    "all_preds_table['model'] = 0\n",
    "\n",
    "for i in range(len(ensemble_preds[1:])):\n",
    "    tmp = ensemble_preds[i].copy()\n",
    "    tmp['model'] = i\n",
    "    all_preds_table = all_preds_table.append(tmp)\n",
    "    \n",
    "all_preds_table.to_csv('toi_ensemble_preds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "agg_preds = {}\n",
    "\n",
    "for preds in ensemble_preds:\n",
    "    for tic_id in preds.index:\n",
    "        if tic_id not in agg_preds:\n",
    "            agg_preds[tic_id] = []\n",
    "\n",
    "        row = preds[preds.index == tic_id]\n",
    "        pred_v = row.values[0]\n",
    "        if len(row.values) > 1:\n",
    "            print(f'Warning: duplicate predictions for {tic_id}')\n",
    "        if pred_v[col_p] >= thresh:\n",
    "            agg_preds[tic_id].append('disp_p')\n",
    "        else:\n",
    "            agg_preds[tic_id].append(preds.columns[np.argmax(pred_v)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_preds = []\n",
    "for tic_id in list(agg_preds.keys()):\n",
    "    counts = {l: 0 for l in labels}\n",
    "    for e in agg_preds[tic_id]:\n",
    "        counts[e] += 1\n",
    "    maxcount = max(counts.values())\n",
    "    counts.update({\n",
    "        'tic_id': tic_id,\n",
    "        'maxcount': maxcount,\n",
    "    })\n",
    "    final_preds.append(counts)\n",
    "    \n",
    "final_preds = pd.DataFrame(final_preds).set_index('tic_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "tce_table = pd.read_csv(tces_file, header=0).set_index('tic_id')\n",
    "tce_labels = tce_table[labels]\n",
    "\n",
    "pl = final_preds.join(tce_labels, on='tic_id', how='left', lsuffix='_p')\n",
    "\n",
    "pl.head()\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.9788732394366197\n",
      "Precision: 0.8128654970760234\n"
     ]
    }
   ],
   "source": [
    "ppos = (pl['disp_p_p'] > 0)\n",
    "pos = (pl['disp_p'] > 0)\n",
    "\n",
    "pneg = (pl['disp_p_p'] == 0)\n",
    "neg = (pl['disp_p'] == 0)\n",
    "\n",
    "print('Recall:', len(pl[ppos & pos]) / len(pl[pos]))\n",
    "print('Precision:', len(pl[ppos & pos]) / len(pl[ppos]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293962038\n",
      "29984131\n",
      "375506058\n"
     ]
    }
   ],
   "source": [
    "for i in pl[pos & pneg][[]].join(tce_table, on='tic_id', how='left').index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119774374\n",
      "23826331\n",
      "24491836\n",
      "276645751\n",
      "276936320\n",
      "278225267\n",
      "278866211\n",
      "280095254\n",
      "30275619\n",
      "304729846\n",
      "307153959\n",
      "344978642\n",
      "380783252\n",
      "384068226\n",
      "384744828\n",
      "424876542\n",
      "469782185\n",
      "74934381\n",
      "98350979\n",
      "137778682\n",
      "365494323\n",
      "388076435\n",
      "149854222\n",
      "2234723\n",
      "262173985\n",
      "321828403\n",
      "24004619\n",
      "457138169\n",
      "255994336\n",
      "1715469662\n",
      "1715469667\n",
      "272783172\n"
     ]
    }
   ],
   "source": [
    "for i in pl[neg & ppos][[]].join(tce_table, on='tic_id', how='left').index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disp_p</th>\n",
       "      <th>disp_e</th>\n",
       "      <th>disp_n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tic_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>344978642</th>\n",
       "      <td>0.853651</td>\n",
       "      <td>0.124928</td>\n",
       "      <td>0.021421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344978642</th>\n",
       "      <td>0.964726</td>\n",
       "      <td>0.026789</td>\n",
       "      <td>0.008486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344978642</th>\n",
       "      <td>0.425970</td>\n",
       "      <td>0.563948</td>\n",
       "      <td>0.010082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344978642</th>\n",
       "      <td>0.997266</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>0.000565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344978642</th>\n",
       "      <td>0.956658</td>\n",
       "      <td>0.033481</td>\n",
       "      <td>0.009861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344978642</th>\n",
       "      <td>0.901508</td>\n",
       "      <td>0.076828</td>\n",
       "      <td>0.021664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344978642</th>\n",
       "      <td>0.994333</td>\n",
       "      <td>0.004218</td>\n",
       "      <td>0.001449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344978642</th>\n",
       "      <td>0.940626</td>\n",
       "      <td>0.047376</td>\n",
       "      <td>0.011998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344978642</th>\n",
       "      <td>0.954688</td>\n",
       "      <td>0.034180</td>\n",
       "      <td>0.011132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344978642</th>\n",
       "      <td>0.934402</td>\n",
       "      <td>0.049931</td>\n",
       "      <td>0.015666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             disp_p    disp_e    disp_n\n",
       "tic_id                                 \n",
       "344978642  0.853651  0.124928  0.021421\n",
       "344978642  0.964726  0.026789  0.008486\n",
       "344978642  0.425970  0.563948  0.010082\n",
       "344978642  0.997266  0.002169  0.000565\n",
       "344978642  0.956658  0.033481  0.009861\n",
       "344978642  0.901508  0.076828  0.021664\n",
       "344978642  0.994333  0.004218  0.001449\n",
       "344978642  0.940626  0.047376  0.011998\n",
       "344978642  0.954688  0.034180  0.011132\n",
       "344978642  0.934402  0.049931  0.015666"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compare(ensemble_preds, filter):\n",
    "    result = ensemble_preds[0][filter]\n",
    "    for preds in ensemble_preds[1:]:\n",
    "        result = result.append(preds[filter])\n",
    "    return result\n",
    "\n",
    "compare(ensemble_preds, preds.index == 344978642)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disp_p_p</th>\n",
       "      <th>disp_e_p</th>\n",
       "      <th>disp_n_p</th>\n",
       "      <th>maxcount</th>\n",
       "      <th>disp_p</th>\n",
       "      <th>disp_e</th>\n",
       "      <th>disp_n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tic_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           disp_p_p  disp_e_p  disp_n_p  maxcount  disp_p  disp_e  disp_n\n",
       "tic_id                                                                   \n",
       "113428669        10         0         0        10       1       0       0"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[pl.index == 113428669]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PR curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "tics = set(ensemble_preds[0].index.values)\n",
    "\n",
    "tic_index = {v: i for i, v in enumerate(tics)}\n",
    "\n",
    "pred_ps = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "pred_es = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "pred_ns = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "for i, preds in enumerate(ensemble_preds):\n",
    "    for row in preds.iterrows():\n",
    "        tic, pred_p = row[0], row[1][col_p]\n",
    "        pred_ps[i][tic_index[tic]] = pred_p\n",
    "        pred_es[i][tic_index[tic]] = row[1][labels.index('disp_e')]\n",
    "        pred_ns[i][tic_index[tic]] = row[1][labels.index('disp_n')]\n",
    "\n",
    "lbl_ps = np.zeros([len(tic_index)], dtype=np.bool)\n",
    "bad_labels = []\n",
    "for row in tce_labels.iterrows():\n",
    "    tic, lbl_p = row[0], row[1]['disp_p']\n",
    "    if tic in tic_index:\n",
    "        lbl_ps[tic_index[tic]] = (lbl_p > 0)\n",
    "    else:\n",
    "        bad_labels.append(tic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cond_pos = float(np.sum(lbl_ps))\n",
    "\n",
    "def pr_at_th(th):\n",
    "    pred_pos = np.any(pred_ps >= th, axis=0)\n",
    "    true_pos = pred_pos & lbl_ps\n",
    "    num_pred_pos = int(np.sum(pred_pos))\n",
    "    num_true_pos = int(np.sum(true_pos))\n",
    "    if num_pred_pos == 0:\n",
    "        return 1.0, 0.0\n",
    "    return float(num_true_pos) / float(num_pred_pos), float(num_true_pos) / float(num_cond_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.9692879278417341, max R: 1.0, max P: 1.0\n",
      "100% recall at: 76%, threshold: 0.1579816417694639\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x740 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "ps, rs, ths = ([], [], [])\n",
    "th = np.max(pred_ps)\n",
    "while th >= 0.0:\n",
    "    p, r = pr_at_th(th)\n",
    "    if p > 0:\n",
    "        ps.append(p)\n",
    "        rs.append(r)\n",
    "    else:\n",
    "        ps.append(1.0)\n",
    "        rs.append(0.0)\n",
    "    ths.append(th)\n",
    "    th -= 0.0005\n",
    "    \n",
    "from sklearn import metrics\n",
    "\n",
    "print(f'AUC: {metrics.auc(rs, ps)}, max R: {max(rs)}, max P: {max(ps)}')\n",
    "    \n",
    "i = len(rs) - 1\n",
    "while rs[i] == 1.0:\n",
    "    i -= 1\n",
    "i += 1\n",
    "print(f'100% recall at: {int(ps[i] * 100)}%, threshold: {ths[i]}')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 3.7), dpi=200)\n",
    "\n",
    "ax.spines['top'].set_color('#808080')\n",
    "ax.spines['right'].set_color('#808080')\n",
    "ax.spines['left'].set_color('#808080')\n",
    "ax.spines['bottom'].set_color('#808080')\n",
    "ax.tick_params(direction='in', color='#808080')\n",
    "\n",
    "plt.grid(color='#c0c0c0', linestyle='--', linewidth=0.5)\n",
    "\n",
    "plt.ylabel('Precision', fontweight='bold')\n",
    "plt.xlabel('Recall', fontweight='bold')\n",
    "\n",
    "plt.xlim((0.0, 1.0))\n",
    "plt.ylim((0.0, 1.0))\n",
    "\n",
    "_ = plt.plot(rs, ps, marker='.')\n",
    "_ = plt.plot([0.0, 1.0], [1.0, 0.0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
